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A discriminative approach for identifying domain–domain interactions from protein–protein interactions
Author(s) -
Zhao XingMing,
Chen Luonan,
Aihara Kazuyuki
Publication year - 2010
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22643
Subject(s) - discriminative model , computer science , benchmark (surveying) , identification (biology) , domain (mathematical analysis) , feature selection , set (abstract data type) , machine learning , artificial intelligence , feature (linguistics) , selection (genetic algorithm) , computational biology , protein–protein interaction , data mining , biology , mathematics , genetics , mathematical analysis , linguistics , philosophy , botany , geodesy , programming language , geography
Protein domains are functional and structural units of proteins. Therefore, identification of domain-domain interactions (DDIs) can provide insight into the biological functions of proteins. In this article, we propose a novel discriminative approach for predicting DDIs based on both protein-protein interactions (PPIs) and the derived information of non-PPIs. We make a threefold contribution to the work in this area. First, we take into account non-PPIs explicitly and treat the domain combinations that can discriminate PPIs from non-PPIs as putative DDIs. Second, DDI identification is formalized as a feature selection problem, in which it tries to find out a minimum set of informative features (i.e., putative DDIs) that discriminate PPIs from non-PPIs, which is plausible in biology and is able to predict DDIs in a systematic and accurate manner. Third, multidomain combinations including two-domain combinations are taken into account in the proposed method, where multidomain cooperations may help proteins to interact with each other. Numerical results on several DDI prediction benchmark data sets show that the proposed discriminative method performs comparably well with other top algorithms with respect to overall performance, and outperforms other methods in terms of precision. The PPI data sets used for prediction of DDIs and prediction results can be found at http://csb.shu.edu.cn/dipd.